Exploring the Relationship between User Activities and Profile Images on Twitter through Machine Learning Techniques

2018 ◽  
Vol 5 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Tomu Tominaga ◽  
Yoshinori Hijikata
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Relationship ◽  
User Activities

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

scholarly journals Age of the entrepreneurial decision: Differences among developed, developing, and non- developed countries

2018 ◽  
Vol 7 (1) ◽  
pp. 36 ◽  
Author(s):  
Alicia Coduras ◽  
Jorge Velilla ◽  
Raquel Ortega
Keyword(s):  
Machine Learning ◽  
Social Norms ◽  
Survey Data ◽  
Population Survey ◽  
Adult Population ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Entrepreneurial Decision ◽  
The Relationship

Although entrepreneurship is widely considered an engine of growth, it is not clear whether policies, de facto, promote it, and knowing which individuals are willing to become entrepreneurs could help in the design of those policies. In this paper, we study how individuals become entrepreneurs at different ages, according to the degree of development of the country of residence. We make use of the GEM 2014 Adult Population Survey data, against a background where social norms are controlled, to find that the relationship between entrepreneurship and age follows an inverted U-shape, according to machine learning techniques, and that younger individuals are the most willing to become entrepreneurs.

scholarly journals Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues

Soft Matter ◽  
2021 ◽  
Author(s):  
Indrajit Tah ◽  
Tristan Sharp ◽  
Andrea Liu ◽  
Daniel Marc Sussman
Keyword(s):  
Machine Learning ◽  
Local Structure ◽  
Biological Tissues ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
Glass Forming ◽  
Learning Techniques ◽  
Glass Forming Materials ◽  
The Relationship

Machine learning techniques have been used to quantify the relationship between local structural features and variations in local dynamical activity in disordered glass-forming materials. To date these methods have been...

scholarly journals Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lasini Wickramasinghe ◽  
Rukmal Weliwatta ◽  
Piyal Ekanayake ◽  
Jeevani Jayasinghe
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Rice Yield ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Climatic Data ◽  
Climate Variables ◽  
Squared Error ◽  
Learning Techniques ◽  
The Relationship

This paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). The performance of each model was assessed in terms of the mean squared error (MSE), correlation coefficient (R), mean absolute percentage error (MAPE), root mean squared error ratio (RSR), BIAS value, and the Nash number, and it was found that the GPR-based model is the most accurate among them. Climate data collected until early 2019 (Maha season of year 2018) were used to develop the model, and an independent validation was performed by applying data of the Yala season of year 2019. The developed model can be used to forecast the future rice yield with very high accuracy.

Machine Learning for Agents and Multi-Agent Systems

2011 ◽  
pp. 1-26
Author(s):  
Daniel Kudenko ◽  
Dimitar Kazakov ◽  
Eduardo Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Multi Agent ◽  
The Relationship

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

Machine Learning for Agents and Multi-Agent Systems

2011 ◽  
pp. 403-420
Author(s):  
Daniel Kudenko ◽  
Dimitar Kazakov ◽  
Eduardo Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Multi Agent ◽  
The Relationship

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

scholarly journals Pattern Discovery in White Etching Crack Experimental Data Using Machine Learning Techniques

2019 ◽  
Vol 9 (24) ◽  
pp. 5502 ◽  
Author(s):  
Baher Azzam ◽  
Freia Harzendorf ◽  
Ralf Schelenz ◽  
Walter Holweger ◽  
Georg Jacobs
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Feature Selection Method ◽  
Chemical Compounds ◽  
Machine Learning Techniques ◽  
Oil Composition ◽  
The Past ◽  
Learning Techniques ◽  
Physical Tests ◽  
The Relationship

White etching crack (WEC) failure is a failure mode that affects bearings in many applications, including wind turbine gearboxes, where it results in high, unplanned maintenance costs. WEC failure is unpredictable as of now, and its root causes are not yet fully understood. While WECs were produced under controlled conditions in several investigations in the past, converging the findings from the different combinations of factors that led to WECs in different experiments remains a challenge. This challenge is tackled in this paper using machine learning (ML) models that are capable of capturing patterns in high-dimensional data belonging to several experiments in order to identify influential variables to the risk of WECs. Three different ML models were designed and applied to a dataset containing roughly 700 high- and low-risk oil compositions to identify the constituting chemical compounds that make a given oil composition high-risk with respect to WECs. This includes the first application of a purpose-built neural network-based feature selection method. Out of 21 compounds, eight were identified as influential by models based on random forest and artificial neural networks. Association rules were also mined from the data to investigate the relationship between compound combinations and WEC risk, leading to results supporting those of previous analyses. In addition, the identified compound with the highest influence was proved in a separate investigation involving physical tests to be of high WEC risk. The presented methods can be applied to other experimental data where a high number of measured variables potentially influence a certain outcome and where there is a need to identify variables with the highest influence.

scholarly journals Mapping Through Listening

2014 ◽  
Vol 38 (3) ◽  
pp. 34-48 ◽  
Author(s):  
Baptiste Caramiaux ◽  
Jules Françoise ◽  
Norbert Schnell ◽  
Frédéric Bevilacqua
Keyword(s):  
Machine Learning ◽  
Design Process ◽  
Design Principle ◽  
Machine Learning Techniques ◽  
Digital Music ◽  
Learning Techniques ◽  
The Relationship ◽  
Interactive Installations ◽  
Perception Action ◽  
Fundamental Design

Gesture-to-sound mapping is generally defined as the association between gestural and sound parameters. This article describes an approach that brings forward the perception–action loop as a fundamental design principle for gesture–sound mapping in digital music instrument. Our approach considers the processes of listening as the foundation—and the first step—in the design of action–sound relationships. In this design process, the relationship between action and sound is derived from actions that can be perceived in the sound. Building on previous work on listening modes and gestural descriptions, we propose to distinguish between three mapping strategies: instantaneous, temporal, and metaphorical. Our approach makes use of machine-learning techniques for building prototypes, from digital music instruments to interactive installations. Four different examples of scenarios and prototypes are described and discussed.

Special Issue on Machine Learning for Robotics and Swarm Systems

2019 ◽  
Vol 31 (4) ◽  
pp. 519-519
Author(s):  
Masahito Yamamoto ◽  
Takashi Kawakami ◽  
Keitaro Naruse
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Autonomous Robots ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Multi Agent ◽  
The Relationship

In recent years, machine-learning applications have been rapidly expanding in the fields of robotics and swarm systems, including multi-agent systems. Swarm systems were developed in the field of robotics as a kind of distributed autonomous robotic systems, imbibing the concepts of the emergent methodology for extremely redundant systems. They typically consist of homogeneous autonomous robots, which resemble living animals that build swarms. Machine-learning techniques such as deep learning have played a remarkable role in controlling robotic behaviors in the real world or multi-agents in the simulation environment. In this special issue, we highlight five interesting papers that cover topics ranging from the analysis of the relationship between the congestion among autonomous robots and the task performances, to the decision making process among multiple autonomous agents. We thank the authors and reviewers of the papers and hope that this special issue encourages readers to explore recent topics and future studies in machine-learning applications for robotics and swarm systems.

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